Dr. Blostein received computer science degrees from
the University of Illinois (B.Sc. in 1978, Ph.D. in 1987) and
Carnegie Mellon University (M.Sc. in 1980). Since 1988 she has
been a faculty member in the School of Computing at Queen's University.

New Research Area: Fascial Network Computing

I am starting work on a novel form of bioinspired computing -- Fascial Network Computing. Fascia, also called connective tissue, forms a bodywide network that provides structural support, protection, shock absorption and elastic recoil. Fascial tissue exhibits a great diversity of characteristics, ranging from stiff cable-like structures such as tendons to supple sheets such as the superficial fascia under the skin. Fascial tissue is adaptive: it changes characteristics in response to the demands placed on it. For example, chronic sitting in a hunched position causes the fascia in the back of neck to become stiffer: the resulting increase in structural support reduces the muscular effort needed to maintain a forward head position, but comes at the expense of reduced neck mobility.
I propose simulating a Fascial Network as an adaptive tensegrity model. Tensegrity (tensional integrity) is a structural principle popularized by Buckminster Fuller in which isolated components under compression are held in place by a network of components under tension. Tensegrity structures are both strong and flexible due to the dynamic interplay between tension and compression forces. In Fascial Network Computing, the tensioned members provide an abstract representation of tension in fascia (see Fascia: The Tensional Network of The Human Body), with the compressed members representing bone as well as hydrostatic pressure (see The diversity of hydrostatic skeletons). During training of a Fascial Network, simulated external load patterns are repeatedly applied to the network. Adaptation rules dictate how the stiffness of a particular fascial element changes in response to the local load it experiences during training; this mimics the adaptive properties displayed by fascia in living organisms.
Revealing analogies can be drawn with Neural Network Computing, a well-established form of bioinspired computing that has been successfully used in many pattern recognition applications. The pattern of stiffnesses in a trained Fascial Network constitutes a type of distributed memory, analogous to the distributed memory formed by the pattern of connection strengths in a trained Neural Network. Both Neural and Fascial Networks exhibit emergent properties such as global response to local injury. Fascial Network Computing offers insight into the structural responsiveness of a biological system, an intriguing complement to the neural responsiveness modeled by Neural Network Computing.
My students and I are beginning investigation of Fascial Network Computing as a topic within computer science: a novel form of bioinspired computation. The longer-term goal is to increase understanding of the biological mechanisms that maintain homeostasis in a fascial network. This requires collaboration with physiologists, clinicians, manual therapists, mathematicians and mechanical engineers. Increased understanding of network homeostasis is essential for improving treatment of common conditions such as lower back pain, whiplash and concussion. Research on the fascial aftereffects of concussion complements extensive existing studies on the neural aftereffects of concussion. If successful, this work has potential application in post-concussion rehabilitation and in estimating future concussion proneness of a patient.

Here are a few relevant links. (See Tom Flemon's Resources page for a more comprehensive list.)

Research in Graphics Recognition and Document Classification

Dr. Blostein has a long-standing research program in pattern recognition, document analysis, and document classification.
The main research goal is to smooth the interface between paper and electronic versions of documents.
Dr. Blostein and her students have worked on developing computer
technology to read, write, and edit diagram notations such as music notation,
math notation, maps, schematics, and architectural drawings.
The text portions of scanned documents can be analyzed with OCR (Optical
Character Recognition), but further processing
is required to extract the information contained in diagram notations.
Dr. Blostein and her students investigate the
use of techniques such as graph transformation and tree transformation, in
order to construct an interpretation of the 2D arrangement of symbols in a
diagram. Other projects include
classification of documents (applied to biomedical documents and software engineering documents),
and the use of internet searches to
validate document recognition results.
Of special interest to Dr. Blostein is exploration and exploitation of
the relationship between diagram recognition and diagram generation.
Technology for generation is far ahead of technology for recognition: diagram generation software outperforms diagram recognition software; speech generation outperforms speech understanding; computer graphics is ahead of computer vision. There are research opportunities here!

Dr. Blostein has coauthored
Lime,
an editor for music notation. Both Macintosh and IBM PC versions are
available for free trial use.

Dr. Blostein was a plenary speaker at
CICM 2009.
She was Chair of
GREC2001, the Fourth IAPR International Workshop on Graphics Recognition,
Kingston, Ontario, in September, 2001.

Visiting Researchers

Oleg Golubitsky, Postdoctoral Fellow, January 2005 to August 2006.
Research topic: structural representations for document recognition.
This builds on existing work with
Lev Goldfarb on
ETS
(Evolving Transformation Systems).

Sergei Levashkin,
visiting sabbaticant, August 2004 to August 2005.
Research on recognition of cartographic maps.